Image Processing Approach for Grading IVF Blastocyst: A State-of-the-Art Review and Future Perspective of Deep Learning-Based Models

The development of intelligence-based methods and application systems has expanded for the use of quality blastocyst selection in in vitro fertilization (IVF). Significant models on assisted reproductive technology (ART) have been discovered, including ones that process morphological image approaches and extract attributes of blastocyst quality. In this study, (1) the state-of-the-art in ART is established using an automated deep learning approach, applications for grading blastocysts in IVF, and related image processing techniques. (2) Thirty final publications in IVF and deep learning were found by an extensive literature search from databases using several relevant sets of keywords based on papers published in full-text English articles between 2012 and 2022. This scoping review sparks fresh thought in deep learning-based automated blastocyst grading. (3) This scoping review introduces a novel notion in the realm of automated blastocyst grading utilizing deep learning applications, showing that these automated methods can frequently match or even outperform skilled embryologists in particular deep learning tasks. This review adds to our understanding of the procedure for selecting embryos that are suitable for implantation and offers important data for the creation of an automated computer-based system for grading blastocysts that applies deep learning.

Automated summary

The development of intelligence-based methods and application systems has expanded the use of quality blastocyst selection in IVF. In this study, the state-of-the-art in ART is established using an automated deep learning approach, and the scoping review introduces a novel notion in automated blastocyst grading utilizing deep learning applications. These automated methods can match or even outperform skilled embryologists in certain deep learning tasks.